Dr. Syed Haider, Ph.D.

Ph.D., University of North Carolina at Charlotte

http://cs.fredonia.edu/haider/

Ph.D., Optical Science and Engineering, 2012
University of North Carolina at Charlotte, Charlotte, NC, USA

M.Sc., Digital Multimedia and Communication Systems, 2006
University of Strathclyde, Glasgow, UK

Masters in Computer Science (Software Engineering), 2003
SZAB Institute of Science and Technology (SZABIST), Karachi, Pakistan

Bachelors in Computer Science (Software Engineering), 2002
SZAB Institute of Science and Technology (SZABIST), Karachi, Pakistan

Office Hours

  • Tuesday: 11am-12pm
  • Wednesday: 9am-11am
  • Thursday: 11am-12pm
  • or by appointment

Teaching Interests

Data and Communication Networks
Data Structures
Operating Systems
Advanced Computer Networks
Software Defined Networks
C++ Programming and OOP
Python Programming

Research Interests

Machine Learning
Static Code Analysis
Smart Cities
Student Progress Monitoring
Computer Networks
Software Defined Networks

Awards and Honors

  • Excellence in Teaching Award, CIS Department - SUNY Fredonia (2022).
  • Excellence in Teaching Award, CIS Department - SUNY Fredonia (2021).
  • Excellence in Teaching Award, CIS Department, SUNY Fredonia (2020).

Current Research

  • Smart City and IoT Research
  • Static Code Analysis and Code Grading

Professional Membership

  • Member, Association of Computing Machinery (ACM)
  • Senior Member, Institute of Electrical and Electronics Engineers (IEEE)

Intellectual Contributions

  • "Reactive Traffic Congestion Control in Smart cities by using Hierarchical Graph," UCS - Journal of Universal Computer Science (2023).
  • "Smart City Traffic Management for Reducing Congestion," 19th IEEE HONET-ICT Conference (2022).
  • "A Deep Learning Based Classifier for Crack Detection with Robots in Underground Pipes," IEEE HONET 2020 (2021).
  • "Performance Prediction of Computer Science Students in Capstone Software Engineering Course through Educational Data Mining," ASEE Annual Conference (2021).
  • "Protocol Based Deep Intrusion Detection for DoS and DDoS attacks using UNSW-NB15 and Bot-IoT data-sets," IEEE Access (2021).
  • "Intrusion detection in Internet of Things using Supervised Machine Learning based on Application and Transport Layer Features using UNSW-NB15 data-set," EURASIP Journal on Wireless Communications and Networking (2021).
  • "Application of Convolutional Neural Networks for Wild Fire Detection," IEEE SouthEastCon (2020).